Put your firm's data to work.
Every fund sits on a mountain of unstructured data that never makes it into a research pipeline. Ragnerock turns that data into structured, queryable intelligence your team can actually use.
Make speculation cheap
Alternative Data
The value of investigating a new signal is uncertain, but the cost of finding out is high. Ragnerock collapses the cost of exploration so that work that wasn't worth investigating at $50K of effort becomes worth investigating at $2K.
You spend half your time wrangling data instead of studying it. Some hypotheses never get explored because the effort-to-uncertainty ratio is too high. The alternative data vendor you're evaluating charges per-seat fees that don't scale, and their schema doesn't match your research questions.
- Process once, query infinitely. Define your AI workflow to automatically process alternative data as it arrives. The output becomes a queryable research substrate you can join with your existing warehouse.
- Define your own schema. Extract exactly what matters for your research question: sentiment scores, entity mentions, regulatory signals, whatever your hypothesis requires. No vendor lock-in to someone else's data model.
- Replace expensive vendors. Build custom data ingress pipelines for sources that would cost a fortune through traditional providers. Process whatever feeds your models, whether news, filings, transcripts, industry reports, tweets, video, or something else.
Run 10x more experiments for the same budget. When a hypothesis doesn't pan out, you've spent days instead of months. When it does, you already have the production pipeline.
Example: Earnings call sentiment pipeline
Ingest transcripts as they're published → extract management sentiment by topic → flag language changes vs. prior quarter → output structured scores to your warehouse → join with position data for signal validation. Defined once as a workflow, runs automatically every earnings season.
Built for quant workflows
Ragnerock outputs flow directly to your existing data infrastructure, including Snowflake, Databricks, BigQuery, or your own Postgres DB. Join AI-derived features with tick data, fundamentals, or any other signal in your research stack. The research agent understands SQL and can help you explore the data conversationally before you commit to code.
Give your analysts a head start
Document Intelligence
Your analysts are experts at evaluating deals, assessing risk, and synthesizing research. But a significant share of their day goes toward a prerequisite step: extracting structured information from documents before they can begin the actual analysis. Pulling terms from offering memoranda. Combing through credit agreements for covenant language. Reading sell-side reports to find the two pages relevant to a current position.
This extraction work is necessary, but it's not where your team's expertise creates value. And the volume only grows: every new deal, every quarter-end reporting cycle, every morning's batch of broker research means more documents to process before analysis can begin.
Ragnerock handles the extraction step so your team can start from structured data. Define what needs to be pulled — key terms, financial covenants, counterparty obligations, price targets, recommendation changes — and the platform processes documents at scale as they arrive. Every output traces back to the specific page and passage it came from, so your analysts can verify anything that looks off and go straight to the source when they need to dig deeper.
The result: your team spends their time on the judgment calls that actually matter — evaluating terms, spotting anomalies, forming views — instead of the mechanical work of getting data out of PDFs.
Investment Pipeline
Extract key terms, financial summaries, and covenant language from CIMs, prospectuses, and credit agreements as they arrive. Your deal team starts with structured comparisons instead of spending their first hours on manual extraction.
Legal & Contract Review
Parse ISDA schedules, prime brokerage agreements, subscription documents, and side letters. Surface obligations, key dates, and non-standard provisions so your legal and ops teams can focus on reviewing what matters rather than finding it.
Broker Research Triage
Process sell-side reports at scale. Extract price targets, rating changes, earnings estimate revisions, and key thesis points. Flag what's relevant to current positions so your analysts can go straight to the research that moves the needle.
The same approach applies across your document workflow: regulatory filings, quarterly portfolio company reports, compliance documentation, and any other recurring document type your firm processes.
"Every insight traced back to source. Every action logged. Every finding defensible."
Make the mandatory cheaper
Surveillance & Compliance
Regulatory obligations require processing and analyzing large volumes of communications, trade records, filings, and other data. This work must be done regardless of cost. The question is whether you do it efficiently.
Ragnerock automates these workflows at a fraction of current costs. Define your own compliance logic — flagging criteria, escalation rules, cross-referencing patterns — and the platform applies it consistently across every communication, trade record, and filing. Every finding is fully traceable to its source.
When regulators ask where a conclusion came from, you won't be scrambling to reconstruct the analysis. The complete audit trail is already there: every message processed, every rule applied, every human review captured.
- Audit trail
- Complete
- Data residency
- BYODB
- Authentication
- SSO
- Encryption
- AES-256
Build your competitive moat
Proprietary Models & Signals
Some analytical tasks are central enough to your strategy that you need a model built specifically for your firm's requirements. A custom classifier for your sector taxonomy. A specialized extractor tuned to the document types your desk works with. A scoring model trained on your team's historical judgments. Building these models requires high-quality labeled data, and generating those labels at scale has traditionally been the bottleneck.
Ragnerock's annotation workflows let you leverage frontier LLMs to generate initial labels, then refine with human review. The same pipeline you use to create training data continues running in production — so there's no gap between how your model was trained and how it operates. When your model makes an unexpected call in production, the provenance system traces it back to the training examples that shaped it.
Your taxonomy, enforced
Define any annotation structure with JSON Schema. Your sector classifications, your risk categories, your entity types. The schema is yours.
Label at scale
Use frontier LLMs to generate initial annotations across thousands of documents. Target human review where it matters most.
Training to production, no handoff
Deploy your trained model as an operator in Ragnerock. The annotation pipeline becomes the production pipeline. Same data flow, same provenance, no reimplementation.
See how Ragnerock fits your firm's workflow.
Every fund's data challenges are different. Walk us through yours and we'll show you what Ragnerock can do with it.